The present invention relates to speech and text recognition, and more particularly to identifying an accurate transcription of written text or speech.
Automatic systems for information extraction, decision making, theorem proving, and query answering usually perform complex calculations involving large knowledge bases and a corpus of deduction rules. The quality of the answer provided by an expert system is strongly correlated to the accuracy of the inputs. In many cases, the inputs are uncertain because they are the output of probabilistic systems, such as voice recognition, or statistical methods providing hypotheses. Assuming the most probable value for each input as the “correct” one is not optimal because alternate input combinations which could have generated a better result are discarded.
Because probabilistic inputs may generate a large number of input combinations, processing every one of the combinations is not feasible for computational complexity reasons. Limiting the number of alternatives for each input increases the probability of missing good answers, while still permitting the possibility of a large searching space that makes finding a solution infeasible. As one example, for a system that has only 50 inputs, limiting each input to only two alternatives generates a significant number (i.e., 250) of scenarios. Known techniques that use semantic analysis to assign scores to words or word sequences must limit the number of inputs to low numbers, such as three to six, in order to keep the number of combinations of inputs at a manageable level.